Grammys Project

Are you excited to dive into data work for an exciting project at The Recording Academy? You know, the non-profit organization behind the Grammy Awards!

In this project, you'll work on real data from both websites owned by The Recording Academy, the non-profit organization behind the famous Grammy Awards. As you just learned, Ray Starck, the VP of Digital Strategy, decided to split the websites into grammy.com and recordingacademy.com to better serve the Recording Academy's various audience needs.

Now, you are tasked with examining the impact of splitting up the two websites, and analyzing the data for a better understanding of trends and audience behavior on both sites.

Are you ready?!?!

Let's do this!

Data Dictionary

To start, you will be working with two files, grammys_live_web_analytics.csv and ra_live_web_analytics.csv.

These files will contain the following information:

• date - The date the data was confirmed. It is in yyyy-mm-dd format.
• visitors - The number of users who went on the website on that day.
• pageviews - The number of pages that all users viewed on the website.
• sessions - The total number of sessions on the website. A session is a group of user interactions with your website that take place within a given time frame. For example a single session can contain multiple page views, events, social interactions.
• bounced_sessions - The total number of bounced sessions on the website. A bounced session is when a visitor comes to the website and does not interact with any pages / links and leaves.
• avg_session_duration_secs - The average length for all session durations for all users that came to the website that day.
• awards_week - A binary flag if the dates align with marketing campaigns before and after the Grammys award ceremony was held. This is the big marketing push to get as many eyeballs watching the event.
• awards_night - The actual night that Grammy Awards event was held.

Part I - Exploratory Data Analysis

Task 1

Import the pandas,numpy, and plotly.express libraries.

# Import libraries
import pandas as pd
import numpy as np
import plotly.express as px

# RUN THIS CELL - DO NOT MODIFY
# this formats numbers to two decimal places when shown in pandas
pd.set_option('display.float_format', lambda x: '%.2f' % x)

Task 2

Load in the first two files for your analysis. They are the grammy_live_web_analytics.csv and ra_live_web_analytics.csv.

A. For the grammy_live_web_analytics.csv file store that into a dataframe called full_df

B. For the ra_live_web_analytics.csv file store that into a dataframe called rec_academy

C. Preview the dataframes to familiarize yourself with the data.

All files needed can be found in the datasets folder.

# Read in dataframes
full_df = pd.read_csv('datasets/grammy_live_web_analytics.csv')
rec_academy = pd.read_csv('datasets/ra_live_web_analytics.csv')

# preview full_df dataframe
print("Preview of full_df:")
print(full_df.head())

Preview of full_df:
         date  visitors  pageviews  sessions  bounced_sessions  \
0  2017-01-01      9611      21407     10196              6490   
1  2017-01-02     10752      25658     11350              7055   
2  2017-01-03     11425      27062     12215              7569   
3  2017-01-04     13098      29189     13852              8929   
4  2017-01-05     12234      28288     12990              8105   

   avg_session_duration_secs  awards_week  awards_night  
0                         86            0             0  
1                        100            0             0  
2                         92            0             0  
3                         90            0             0  
4                         95            0             0  

# preview rec_academy dataframe
print("\nPreview of rec_academy:")
print(rec_academy.head())

Preview of rec_academy:
         date  visitors  pageviews  sessions  bounced_sessions  \
0  2022-02-01       928       2856      1092               591   
1  2022-02-02      1329       3233      1490               923   
2  2022-02-03      1138       3340      1322               754   
3  2022-02-04       811       2552       963               534   
4  2022-02-05       541       1530       602               326   

   avg_session_duration_secs  awards_week  awards_night  
0                        148            0             0  
1                         90            0             0  
2                        127            0             0  
3                        142            0             0  
4                        111            0             0  

Task 3

We all know The Grammy Awards is the biggest music event in the music industry, but how many visitors does that bring to the website?

A. Create a line chart of the number of users on the site for every day in the full_df. See if you can spot the days the Grammys awards are hosted.

# Plot a line chart of the visitors on the site.
fig = px.line(full_df, 
              x='date', 
              y='visitors', 
              title='Daily Visitors to the Website',
              labels={'date': 'Date', 'visitors': 'Number of Visitors'})
fig.update_traces(line=dict(color='blue'))  # Optional styling
fig.show()

Remark: The smaller spikes, typically around November/December of each year, are when the nominees are announced.

B. What can you say about the visitors to the website by looking at the graph?

When the grammys are soon arriving, then that is when most visitors show up.

Task 4

Let's investigate what an "average" day looks like when the awards show is being hosted versus the other 364 days out of the year.

A. Use the pandas .groupby() to calculate the number of visitors on the site based on the values in the column awards_night.

avg_visitors_by_awards_night = full_df.groupby('awards_night')['visitors'].mean()


print("Average Number of Visitors (Grouped by awards_night):")
print(avg_visitors_by_awards_night)

Average Number of Visitors (Grouped by awards_night):
awards_night
0     32388.28
1   1389590.23
Name: visitors, dtype: float64

B. What can you say about these results? Roughly how many more visitors are on the website for the awards ceremony versus a regular day?

When the award nights are present then that is when there is a significant jump of visitors on the website

Remark: This is The Recording Academy's biggest challenge! How do you transform a business that relies on the success of one event per year into one that continues to bring users back on the site year round?

Task 5

When The Recording Academy decided to split their website into two domains, grammy.com and recordingacademy.com, the data capture for grammy.com was not affected. So the full_df variable needs to be split separately into two dataframes. The day the domains were switched is on 2022-02-01.

Create two new dataframes:

1. combined_site for all dates before 2022-02-01
2. grammys for all dates after (and including) 2022-02-01

# Split the data to separate the full_df into two new dataframes.
# One for before the switch of the websites and one for after

full_df['date'] = pd.to_datetime(full_df['date'])

combined_site = full_df[full_df['date'] < '2022-02-01']
grammys = full_df[full_df['date'] >= '2022-02-01']

# Run the following cell - DO NOT MODIFY
# .copy() prevents pandas from printing a scary-looking warning message
combined_site = combined_site.copy()
grammys = grammys.copy()

# print the shape of the combined_site dataframe
print("Shape of combined_site dataframe:", combined_site.shape)

Shape of combined_site dataframe: (1857, 8)

If done correctly, the combined_site dataframe should have a total of 1857 rows and 8 columns

Part II - It's all about KPIs

There are certain key performance indicators (KPIs) of interest for The Recording Academy. Let's investigate those a little more.

Task 6

A. Create a new list called frames that has the combined_site, rec_academy, and grammys dataframes as entries. e.g. If the 3 dataframes were df1, df2, and df3, then the code would look like:

frames = [df1, df2, df3]

# create the list of dataframes
frames = [combined_site, rec_academy, grammys]

B. For each frame in the frames list, create a new column pages_per_session. This new column is the average number of pageviews per session on a given day. The higher this number the more "stickiness" your website has with your visitors.

Hint: Divide the pageviews by sessions

This can be achieved by using the following template:

for frame in frames:
    frame['new_col'] = frame['col_A'] / frame['col_B']

# create the `pages_per_session` column for all 3 dataframes.
for frame in frames:
    frame['pages_per_session'] = frame['pageviews'] / frame['sessions']

C. Visualize this new metric using a line chart for each site. (You will have 3 separate graphs)

# combined_site graph
fig_combined = px.line(combined_site, 
                       x='date', 
                       y='pages_per_session', 
                       title='Pages Per Session (Combined Site)',
                       labels={'date': 'Date', 'pages_per_session': 'Pages Per Session'})
fig_combined.show()

# grammys graph
fig_grammys = px.line(grammys, 
                      x='date', 
                      y='pages_per_session', 
                      title='Pages Per Session (Grammys)',
                      labels={'date': 'Date', 'pages_per_session': 'Pages Per Session'})
fig_grammys.show()

# rec_academy graph
fig_rec_academy = px.line(rec_academy, 
                          x='date', 
                          y='pages_per_session', 
                          title='Pages Per Session (Recording Academy)',
                          labels={'date': 'Date', 'pages_per_session': 'Pages Per Session'})
fig_rec_academy.show()

D. Looking at the 3 charts above, what can you say about the pages_per_session when the websites were combined versus after they were split?

Note: Any large spikes in the data that do not correspond with the Grammy Awards Ceremony can be attributed to abnormalities in the data collection process and ignored in your analysis.

When they were combined , the graph showed consistent behavior but when they were split, the graphs show spikes during the days of the awards.

Task 7

Bounce rate is another important metric for The Recording Academy. Bounce Rate is a measure of the percentage of visitors who come to the site and never interact with the website and leave. In this task, you will define a function that takes in a dataframe as input and outputs the bounce rate.

A. Create a function called bounce_rate that:

1. Takes in a dataframe as input
2. adds up all of the values in the bounced_sessions column and stores in a variable called sum_bounced
3. adds up all of the values in the sessions column and stores it in a variable called sum_sessions
4. returns 100 * sum_bounced / sum_sessions

Hint: You will need use the .sum() function both in the sum_bounced and sum_sessions calculations. Don't forget to multiply by 100 so that the answer appears as a percentage instead of a decimal.

def bounce_rate(dataframe):
    '''
    Calculates the bounce rate for visitors on the website.
    input: dataframe with bounced_sessions and sessions columns
    output: numeric value from bounce rate
    '''
    # WRITE YOUR CODE BELOW
    # Remember, the input for the function is called `dataframe`
    # So all calculations should reference that variable.
    sum_bounced = dataframe['bounced_sessions'].sum()
    sum_sessions = dataframe['sessions'].sum()
    return 100 * sum_bounced / sum_sessions

B. Use the frames variable from Task 6 to loop over each website (represented by a dataframe) to calculate the bounce rate. Print the bounce rate for each site.

A template for getting the function to work will look like code below. Remember that this is NOT the print statement, you will still need to add that part.

Hint: To get the bounce rate use bounce_rate(frame)

for frame in frames:
    my_value = my_function(frame)

Tip: If you want to reduce the number of decimals shown in an f-string, you can add :0.2f just before the end of the curly brackets but after your variable. Example: print(f'my value is: {my_value:0.2f}')

# Calculate the Bounce Rate for each site. Use the frames list you created in Task 6.
for frame, name in zip(frames, ['Combined Site', 'Recording Academy', 'Grammys']):
    rate = bounce_rate(frame)
    print(f'{name} Bounce Rate: {rate:0.2f}%')

Combined Site Bounce Rate: 41.58%
Recording Academy Bounce Rate: 33.67%
Grammys Bounce Rate: 40.16%

If done correctly, the combined_site and grammys site will each have bounce rates in the low 40s. The rec_academy will have a bounce rate in the low 30s

C. Another useful metric is how long on average visitors are staying on the website.

Calculate the mean of the avg_session_duration_secs for each of the sites. Print each one using an f-string.

# Calculate the average of the avg_session_duration_secs. Use the frames list you created in Task 6.
for frame, name in zip(frames, ['Combined Site', 'Recording Academy', 'Grammys']):
    rate = bounce_rate(frame)
    print(f'{name} Bounce Rate: {rate:0.2f}%')

Combined Site Bounce Rate: 41.58%
Recording Academy Bounce Rate: 33.67%
Grammys Bounce Rate: 40.16%

D. What can you say about these two metrics as it relates to each of the websites?

The split allows for more focused visitor behaivior and easier to track but it seems that most visitors check both options at the same time.

for frame, name in zip(frames, ['Combined Site', 'Recording Academy', 'Grammys']): rate = bounce_rate(frame) print(f'{name} Bounce Rate: {rate:0.2f}%')# Part III - Demographics

Age demographics are a way to see which audience(s) your content is resonating with the most. This can inform marketing campaigns, ads, and much more.

Let's investigate the demographics for the two websites. This will require reading in two new files and joining them in python.

Task 8

The grammys_age_demographics.csv and tra_age_demographics.csv each contain the following information:

• age_group - The age group range. e.g. 18-24 are all visitors between the ages of 18 to 24 who come to the site.
• pct_visitors - The percentage of all of the websites visitors that come from that specific age group.

A. Read in the grammys_age_demographics.csv and tra_age_demographics.csv files and store them into dataframes named age_grammys and age_tra, respectively.

# read in the files
age_grammys = pd.read_csv('datasets/grammys_age_demographics.csv')
age_tra = pd.read_csv('datasets/tra_age_demographics.csv')

# preview the age_grammys file. the age_tra will look very similar.
print(age_grammys.head())
print(age_tra.head())

  age_group  pct_visitors
0     18-24         27.37
1     25-34         24.13
2     35-44         18.72
3     45-54         13.57
4     55-64          9.82
  age_group  pct_visitors
0     18-24         27.12
1     25-34         26.16
2     35-44         19.55
3     45-54         13.82
4     55-64          8.24

B. For each dataframe, create a new column called website whose value is the name of the website. e.g. the age_grammys values for website should all be Grammys and for the age_tra they should be Recording Academy.

# create the website column
age_grammys['website'] = 'Grammys'
age_tra['website'] = 'Recording Academy'

C. use the pd.concat() method to join these two datasets together. Store the result into a new variable called age_df

Hint: Remember that you need to put your dataframe variables inside of a list first then pass that as your input of pd.concat()

# use pd.concat to join the two datasets
age_df = pd.concat([age_grammys, age_tra])
print(age_df.shape)  # Should output (12, 3)
print(age_df.head())  # Preview the combined dataframe

(12, 3)
  age_group  pct_visitors  website
0     18-24         27.37  Grammys
1     25-34         24.13  Grammys
2     35-44         18.72  Grammys
3     45-54         13.57  Grammys
4     55-64          9.82  Grammys

If done correctly your new dataframe will have 12 rows and 3 columns.

D. Create a bar chart of the age_group and pct_visitors. This chart should have, for each age group, one color for the Recording Academy and a different color for the Grammys.

Hint: You will need to use the barmode='group' option in px.bar(). See the code snippet below to guide you.

# template for visualization
px.bar(dataframe, x='variable1', y='variable2', color='variable3', barmode='group')

# Create bar chart
fig_age = px.bar(
    age_df,
    x='age_group',
    y='pct_visitors',
    color='website',
    title='Age Group Distribution Across Websites',
    labels={'age_group': 'Age Group', 'pct_visitors': 'Percentage of Visitors'},
    barmode='group'
)

fig_age.show()

E. Looking at the chart above, what can you say about how the age demographics differ between the two websites?

It would seem that the age demographic is relatively similar throughout both websites.

Part IV - Recommendation

Task 9

Using the work you did in this project, would you recommend that the websites stay separate? Please give a 2-3 paragraph answer using details from the analysis work above explaining why or why not they should stay separate.

I believe that it may be best to combine them into one website. Theres many reasons for this but one of them is that it may be easier to handle and care for one website instead of two. 
Of course if we are concerned about how often visitors check a certain topic, we can check the amount of times the link has been pressed for that topic. Also fusing the two may indirectly cause visitors to check the the section, thus causeing more visits for the topic.

LevelUp

Ray and Harvey are both interested to see how the Grammys.com website compares to that of their main music award competitor, The American Music Awards (AMA). The dashboard below is aggregated information about the performace of The AMA website for the months of April, May, and June of 2023.

Your task is to determine how the Grammys website is performing relative to The AMA website. In particular, you will be looking at the device distribution and total visits over the same time span and leveraging information about Visit Duration, Bounce Rate, and Pages / Visit from your work in the core of this project.

Let's review some of the content from above.

The Total Visits column is the total number of visitors on the website during the timespan given. The Device Distribution is the percentage share of visitors coming from Desktop users (PCs, Macs, etc.) and Mobile Users (iPhone, Android, etc.).

Visitors on the AMA website are spending on average, 5 mins and 53 seconds on the site and viewing 2.74 pages per visit (aka session). They have a bounce rate of 54.31%

A. Load in the two files. The desktop_users.csv and mobile_users.csv files contain the users coming from desktop users and mobile users respectively.

Store them in variables named desktop_users and mobile_users

# Load in the data

# preview the desktop_users file

# preview mobile_users file

As you can imagine, you will be joining the two datasets together! But before you do that, you will modify the column names so that it's easier to use.

B. For each dataframe, change the name of the visitors column so that it says which category they come from. For example, the desktop_users dataframe should have a column named desktop_visitors instead of visitors.

Additionally, drop the segment column since it is no longer needed.

# change name of the visitors column to indicate which category it comes from

# drop the segment column from each dataframe

C. Join the two dataframes together in a new variable called segment_df.

# join the two dataframes and preview the dataframe

D. In the next few steps, you will calculate the percentage share of users coming from desktop and mobile on the Grammys website.

Calculate a new column, total_visitors that is the addition of desktop_visitors and mobile_visitors.

# create total_visitors column

To calculate the percentage share you will first need to filter the data to dates after (and including) 2023-04-01. Then calculate the sum of desktop visitors and total visitors and divide those values. The percentage share of mobile visitors will be the value needed to get to 100%.

# filter and calculate the percentage share

What is the percentage share of desktop and mobile visitors on the Grammys website in the timeframe in question?

What is the total number of visitors on the site during this timeframe?

(Double-click this cell to write your answer)

E. How is the Grammys website performing relative to its competitor? What is the Grammys doing well and what KPIs does it need to improve?

(Double-click this cell to write your answer)